Literature DB >> 32814337

Probability of Major Depression Classification Based on the SCID, CIDI, and MINI Diagnostic Interviews: A Synthesis of Three Individual Participant Data Meta-Analyses.

Yin Wu1,2, Brooke Levis1,3,4, John P A Ioannidis5, Andrea Benedetti3,6,7, Brett D Thombs8,9,10,11,12,13,14.   

Abstract

INTRODUCTION: Three previous individual participant data meta-analyses (IPDMAs) reported that, compared to the Structured Clinical Interview for the DSM (SCID), alternative reference standards, primarily the Composite International Diagnostic Interview (CIDI) and the Mini International Neuropsychiatric Interview (MINI), tended to misclassify major depression status, when controlling for depression symptom severity. However, there was an important lack of precision in the results.
OBJECTIVE: To compare the odds of the major depression classification based on the SCID, CIDI, and MINI.
METHODS: We included and standardized data from 3 IPDMA databases. For each IPDMA, separately, we fitted binomial generalized linear mixed models to compare the adjusted odds ratios (aORs) of major depression classification, controlling for symptom severity and characteristics of participants, and the interaction between interview and symptom severity. Next, we synthesized results using a DerSimonian-Laird random-effects meta-analysis.
RESULTS: In total, 69,405 participants (7,574 [11%] with major depression) from 212 studies were included. Controlling for symptom severity and participant characteristics, the MINI (74 studies; 25,749 participants) classified major depression more often than the SCID (108 studies; 21,953 participants; aOR 1.46; 95% confidence interval [CI] 1.11-1.92]). Classification odds for the CIDI (30 studies; 21,703 participants) and the SCID did not differ overall (aOR 1.19; 95% CI 0.79-1.75); however, as screening scores increased, the aOR increased less for the CIDI than the SCID (interaction aOR 0.64; 95% CI 0.52-0.80).
CONCLUSIONS: Compared to the SCID, the MINI classified major depression more often. The odds of the depression classification with the CIDI increased less as symptom levels increased. Interpretation of research that uses diagnostic interviews to classify depression should consider the interview characteristics.
© 2020 S. Karger AG, Basel.

Entities:  

Keywords:  Classification; Depressive disorders; Diagnostic interviews; Individual participant data meta-analysis; Major depression

Mesh:

Year:  2020        PMID: 32814337      PMCID: PMC8993569          DOI: 10.1159/000509283

Source DB:  PubMed          Journal:  Psychother Psychosom        ISSN: 0033-3190            Impact factor:   17.659


  31 in total

1.  A difference that matters: comparisons of structured and semi-structured psychiatric diagnostic interviews in the general population.

Authors:  T S Brugha; P E Bebbington; R Jenkins
Journal:  Psychol Med       Date:  1999-09       Impact factor: 7.723

2.  Clinimetrics and clinical psychometrics: macro- and micro-analysis.

Authors:  Elena Tomba; Per Bech
Journal:  Psychother Psychosom       Date:  2012-09-06       Impact factor: 17.659

3.  Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): a meta-analysis.

Authors:  Laura Manea; Simon Gilbody; Dean McMillan
Journal:  CMAJ       Date:  2011-12-19       Impact factor: 8.262

4.  The Decline of Pluralism in Medicine: Dissent Is Welcome.

Authors:  Giovanni A Fava
Journal:  Psychother Psychosom       Date:  2019-12-13       Impact factor: 17.659

5.  A comparison of two interview schedules. The Schedule for Affective Disorders and Schizophrenia-Lifetime and the National Institute for Mental Health Diagnostic Interview Schedule.

Authors:  V Hesselbrock; J Stabenau; M Hesselbrock; P Mirkin; R Meyer
Journal:  Arch Gen Psychiatry       Date:  1982-06

6.  Diagnostic validity of the Hospital Anxiety and Depression Scale (HADS) in cancer and palliative settings: a meta-analysis.

Authors:  Alex J Mitchell; Nick Meader; Paul Symonds
Journal:  J Affect Disord       Date:  2010-03-05       Impact factor: 4.839

Review 7.  Methods to identify postnatal depression in primary care: an integrated evidence synthesis and value of information analysis.

Authors:  C Hewitt; S Gilbody; S Brealey; M Paulden; S Palmer; R Mann; J Green; J Morrell; M Barkham; K Light; D Richards
Journal:  Health Technol Assess       Date:  2009-07       Impact factor: 4.014

8.  Identifying depression with the PHQ-2: A diagnostic meta-analysis.

Authors:  Laura Manea; Simon Gilbody; Catherine Hewitt; Alice North; Faye Plummer; Rachel Richardson; Brett D Thombs; Bethany Williams; Dean McMillan
Journal:  J Affect Disord       Date:  2016-06-06       Impact factor: 4.839

9.  The diagnostic accuracy of the Patient Health Questionnaire-2 (PHQ-2), Patient Health Questionnaire-8 (PHQ-8), and Patient Health Questionnaire-9 (PHQ-9) for detecting major depression: protocol for a systematic review and individual patient data meta-analyses.

Authors:  Brett D Thombs; Andrea Benedetti; Lorie A Kloda; Brooke Levis; Ioana Nicolau; Pim Cuijpers; Simon Gilbody; John P A Ioannidis; Dean McMillan; Scott B Patten; Ian Shrier; Russell J Steele; Roy C Ziegelstein
Journal:  Syst Rev       Date:  2014-10-27

10.  Comparison of major depression diagnostic classification probability using the SCID, CIDI, and MINI diagnostic interviews among women in pregnancy or postpartum: An individual participant data meta-analysis.

Authors:  Brooke Levis; Dean McMillan; Ying Sun; Chen He; Danielle B Rice; Ankur Krishnan; Yin Wu; Marleine Azar; Tatiana A Sanchez; Matthew J Chiovitti; Parash Mani Bhandari; Dipika Neupane; Nazanin Saadat; Kira E Riehm; Mahrukh Imran; Jill T Boruff; Pim Cuijpers; Simon Gilbody; John P A Ioannidis; Lorie A Kloda; Scott B Patten; Ian Shrier; Roy C Ziegelstein; Liane Comeau; Nicholas D Mitchell; Marcello Tonelli; Simone N Vigod; Franca Aceti; Rubén Alvarado; Cosme Alvarado-Esquivel; Muideen O Bakare; Jacqueline Barnes; Cheryl Tatano Beck; Carola Bindt; Philip M Boyce; Adomas Bunevicius; Tiago Castro E Couto; Linda H Chaudron; Humberto Correa; Felipe Pinheiro de Figueiredo; Valsamma Eapen; Michelle Fernandes; Barbara Figueiredo; Jane R W Fisher; Lluïsa Garcia-Esteve; Lisa Giardinelli; Nadine Helle; Louise M Howard; Dina Sami Khalifa; Jane Kohlhoff; Laima Kusminskas; Zoltán Kozinszky; Lorenzo Lelli; Angeliki A Leonardou; Beth A Lewis; Michael Maes; Valentina Meuti; Sandra Nakić Radoš; Purificación Navarro García; Daisuke Nishi; Daniel Okitundu Luwa E-Andjafono; Emma Robertson-Blackmore; Tamsen J Rochat; Heather J Rowe; Bonnie W M Siu; Alkistis Skalkidou; Alan Stein; Robert C Stewart; Kuan-Pin Su; Inger Sundström-Poromaa; Meri Tadinac; S Darius Tandon; Iva Tendais; Pavaani Thiagayson; Annamária Töreki; Anna Torres-Giménez; Thach D Tran; Kylee Trevillion; Katherine Turner; Johann M Vega-Dienstmaier; Karen Wynter; Kimberly A Yonkers; Andrea Benedetti; Brett D Thombs
Journal:  Int J Methods Psychiatr Res       Date:  2019-09-30       Impact factor: 4.035

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Authors:  Dipika Neupane; Brooke Levis; Parash M Bhandari; Brett D Thombs; Andrea Benedetti
Journal:  Int J Methods Psychiatr Res       Date:  2021-05-12       Impact factor: 4.182

2.  The Prevalence and Sociodemographic Correlates of Social Anxiety Disorder: A Focused National Survey.

Authors:  Aamal Ambusaidi; Salim Al-Huseini; Hiba Alshaqsi; Manal AlGhafri; Moon-Fai Chan; Nasser Al-Sibani; Samir Al-Adawi; M Walid Qoronfleh
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3.  Association between Type-D Personality and Affective (Anxiety, Depression, Post-traumatic Stress) Symptoms and Maladaptive Coping in Breast Cancer Patients: A Longitudinal Study.

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4.  Mid-term Psychiatric Outcomes of Patients Recovered From COVID-19 From an Italian Cohort of Hospitalized Patients.

Authors:  Carla Gramaglia; Eleonora Gambaro; Mattia Bellan; Piero Emilio Balbo; Alessio Baricich; Pier Paolo Sainaghi; Mario Pirisi; Giulia Baldon; Sofia Battistini; Valeria Binda; Alessandro Feggi; Martina Gai; Eleonora Gattoni; Amalia Jona; Luca Lorenzini; Debora Marangon; Maria Martelli; Pierluigi Prosperini; Patrizia Zeppegno
Journal:  Front Psychiatry       Date:  2021-06-10       Impact factor: 4.157

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